Generation of β cell-specific human cytotoxic T cells by lentiviral transduction and their survival in immunodeficient human leucocyte antigen-transgenic mice
Bibliographic record
Abstract
Several β cell antigens recognized by T cells in the non-obese diabetic (NOD) mouse model of type 1 diabetes (T1D) are also T cell targets in the human disease. While numerous antigen-specific therapies prevent diabetes in NOD mice, successful translation of rodent findings to patients has been difficult. A human leucocyte antigen (HLA)-transgenic mouse model incorporating human β cell-specific T cells might provide a better platform for evaluating antigen-specific therapies. The ability to study such T cells is limited by their low frequency in peripheral blood and the difficulty in obtaining islet-infiltrating T cells from patients. We have worked to overcome this limitation by using lentiviral transduction to 'reprogram' primary human CD8 T cells to express three T cell receptors (TCRs) specific for a peptide derived from the β cell antigen islet-specific glucose-6-phosphatase catalytic subunit-related protein (IGRP265-273 ) and recognized in the context of the human class I major histocompatibility complex (MHC) molecule HLA-A2. The TCRs bound peptide/MHC multimers with a range of avidities, but all bound with at least 10-fold lower avidity than the anti-viral TCR used for comparison. One exhibited antigenic recognition promiscuity. The β cell-specific human CD8 T cells generated by lentiviral transduction with one of the TCRs released interferon (IFN)-γ in response to antigen and exhibited cytotoxic activity against peptide-pulsed target cells. The cells engrafted in HLA-A2-transgenic NOD-scid IL2rγ(null) mice and could be detected in the blood, spleen and pancreas up to 5 weeks post-transfer, suggesting the utility of this approach for the evaluation of T cell-modulatory therapies for T1D and other T cell-mediated autoimmune diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".